129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
import json
|
||
import os
|
||
import pandas as pd
|
||
import numpy as np
|
||
from datetime import timedelta
|
||
|
||
# 1. 整合数据
|
||
# data_dirs = ['data_test_dir', 'data_test_dir1']
|
||
data_dirs = ['data_test_dir']
|
||
all_records = []
|
||
|
||
for d in data_dirs:
|
||
print(f"Processing directory: {d}")
|
||
if not os.path.exists(d):
|
||
print(f"Directory {d} does not exist")
|
||
continue
|
||
print(f"Found directory: {d}")
|
||
files = os.listdir(d)
|
||
print(f"Files in directory: {files}")
|
||
for f in files:
|
||
if f.endswith('.json') and f != 'stat_result.json':
|
||
file_path = os.path.join(d, f)
|
||
print(f"Processing file: {file_path}")
|
||
try:
|
||
with open(file_path, 'r', encoding='utf-8') as file:
|
||
data = json.load(file)
|
||
print(f"Loaded {len(data)} records from {f}")
|
||
if len(data) > 0:
|
||
print(f"First record keys: {list(data[0].keys())}")
|
||
all_records.extend(data)
|
||
except Exception as e:
|
||
print(f"Error processing {file_path}: {str(e)}")
|
||
continue
|
||
|
||
print(f"Total records loaded: {len(all_records)}")
|
||
df = pd.DataFrame(all_records)
|
||
print(f"DataFrame columns: {list(df.columns)}")
|
||
if 'id' in df.columns:
|
||
df = df.drop_duplicates(subset=['id'], keep='last')
|
||
print(f"Total unique records after deduplication: {len(df)}")
|
||
else:
|
||
print("No 'id' column found in DataFrame")
|
||
if 'time' in df.columns:
|
||
df['time'] = pd.to_datetime(df['time'])
|
||
print(f"Total unique records: {len(df)}")
|
||
else:
|
||
print("No 'time' column found in DataFrame")
|
||
print("Sample of first 5 records:")
|
||
print(df.head())
|
||
|
||
|
||
# 2. 极速统计函数
|
||
def calculate_stats_fast(group):
|
||
if group.empty: return {}
|
||
stats = {}
|
||
stats['winner_prob'] = group['winner'].value_counts(normalize=True).to_dict()
|
||
stats['GD1_prob'] = (group['winner'] >= 12).map({True: '冠亚大', False: '冠亚小'}).value_counts(
|
||
normalize=True).to_dict()
|
||
stats['GD2_prob'] = group['GD2'].value_counts(normalize=True).to_dict()
|
||
|
||
res_df = pd.DataFrame(group['result'].tolist())
|
||
pos_probs = {}
|
||
pos_detail_probs = {}
|
||
for col in range(10):
|
||
col_data = res_df[col]
|
||
pos_probs[f'pos_{col}'] = col_data.value_counts(normalize=True).to_dict()
|
||
is_big = (col_data >= 6).map({True: '大', False: '小'})
|
||
is_odd = (col_data % 2 != 0).map({True: '单', False: '双'})
|
||
pos_detail_probs[f'pos_{col}'] = {
|
||
'big_small': is_big.value_counts(normalize=True).to_dict(),
|
||
'odd_even': is_odd.value_counts(normalize=True).to_dict()
|
||
}
|
||
stats['result_pos_prob'] = pos_probs
|
||
stats['result_pos_detail_prob'] = pos_detail_probs
|
||
|
||
glh_df = pd.DataFrame(group['GLH_result'].tolist())
|
||
glh_pos_probs = {}
|
||
for col in range(5):
|
||
glh_pos_probs[f'pos_{col}'] = glh_df[col].value_counts(normalize=True).to_dict()
|
||
stats['GLH_pos_prob'] = glh_pos_probs
|
||
return stats
|
||
|
||
|
||
# 3. 多维度聚合
|
||
df['hour_min'] = df['time'].dt.strftime('%H:%M:%S')
|
||
df['day_of_month'] = df['time'].dt.day
|
||
df['day_of_week'] = df['time'].dt.dayofweek
|
||
|
||
# 全量统计
|
||
print("Calculating full history stats...")
|
||
time_stats = df.groupby('hour_min').apply(calculate_stats_fast).to_dict()
|
||
date_stats = df.groupby('day_of_month').apply(calculate_stats_fast).to_dict()
|
||
week_stats = df.groupby('day_of_week').apply(calculate_stats_fast).to_dict()
|
||
|
||
# 最近 100 天统计
|
||
print("Calculating recent 100 days stats...")
|
||
first_date = df['time'].min()
|
||
last_date = df['time'].max()
|
||
# 总预测命中率大概在 0.3241
|
||
# start_date_last_0000_1d = last_date - timedelta(days=int(len(df["time"]) * 0.3241 / 276))
|
||
# 取前 x 天，多了这 x 天会影响概率分布 + 万份之一
|
||
start_date_last_0000_2d = last_date - timedelta(days=(0.0001 * len(df["time"]) / 276) + 1)
|
||
df_0000_2d = df[df['time'] >= start_date_last_0000_2d]
|
||
time_stats_0000_2d = df_0000_2d.groupby('hour_min').apply(calculate_stats_fast).to_dict()
|
||
|
||
# 4. 保存结果
|
||
output_data = {
|
||
'by_time': time_stats,
|
||
'by_time_recent_0000_2d': time_stats_0000_2d,
|
||
'by_date': date_stats,
|
||
'by_week': week_stats,
|
||
'last_updated': last_date.strftime('%Y-%m-%d %H:%M:%S')
|
||
}
|
||
|
||
# 创建data_test_predict目录（如果不存在）
|
||
predict_dir = 'data_test_predict'
|
||
if not os.path.exists(predict_dir):
|
||
print(f"Creating directory: {predict_dir}")
|
||
os.makedirs(predict_dir)
|
||
else:
|
||
print(f"Directory {predict_dir} already exists")
|
||
|
||
# 保存结果
|
||
output_file = os.path.join(predict_dir, 'aggregated_stats_v7.json')
|
||
print(f"Saving results to: {output_file}")
|
||
with open(output_file, 'w') as f:
|
||
json.dump(output_data, f)
|
||
|
||
print(f"Stats V7 generated with 100-day window. Last data point: {last_date}") |